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Recent disruptions in transportation systems resulting from natural disasters, cyber accidents, and other factors clearly show the fragility of the airports and underscore the need for building resilience. This study introduces a comprehensive framework for evaluating the resilience of airport infrastructure, integrating critical functions and performance indicators in the context of specific missions that the airport needs to achieve. By focusing on the Dallas-Fort Worth International Airport (DFW) as a case study, the paper outlines a multi-criteria decision analysis (MCDA) methodology for identifying and assessing the critical functions of airports as well as their ability to recover and adapt under different threat scenarios including threat-agnostic situation. The methodology and its application to the DFW case study offer insights into the resilience of airport operations, highlighting key areas for improvement and the potential for policy intervention. This study provides a robust tool for airport administrators and policymakers to enhance infrastructure resilience through a detailed analysis and visualization of airport performance indicators, thereby contributing to the broader discourse on transportation system sustainability and disaster preparedness. 

Ga-and In-exchanged chabazite (CHA) zeolites with same Si/Al and metal/Al ratios were prepared via the incipient wetness impregnation method, were characterized using N-2 adsorption, electron microscopy, temperature-programed reactions and were evaluated for the ethane dehydrogenation reaction using flow microreactors. Ga-CHA has higher reaction rates and a lower activation energy of 107 kJ/mol than In-CHA (E-a = 175 kJ/mol). Rietveld refinement of the X-ray powder diffraction pattern shows that the In+ cation is predominantly located above the 6-ring of the CHA cage. It is proposed that the reaction proceeds through the alkyl mechanism based on stability of alkyl hydride intermediates as determined using DFT calculations. The oxidative addition of ethane to the metal shows much lower Gibbs free energy for Ga-CHA (+27.95 kJ/mol) vs In-CHA (+124.85 kJ/mol). These results indicate that oxidative addition may be the rate-limiting step of ethane dehydrogenation in these materials. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.